Engineering Prompt-Orchestrated LLM Workflows for Automated Test Case Generation in Agile Environments

Antonio A.A.F.; Alfredo B.P.; Gustavo S.G.R. · International Journal of Advanced Computer Science and Applications (2025) · DOI: 10.14569/ijacsa.2025.0161272

Resumen

Manual test case generation for agile software development is a criticalbottleneck that is costly, inconsistent, and error-prone. This study introduces a prompt-engineering and multi-level orchestrationframeworkto automate this process. The proposed approach explicitly targets the automated generation of high-level acceptance test cases, addressing a gap in existing research that predominantly focuses on unit-level or reactive testing. The proposed tool, AI-Based Desktop Test Generator (AIDTG), employs a dual-LLM engine (Gemini 1.5 and GPT-4) to transform high-level functional descriptions from the Product Backlog into structured validation scenarios. Unlike prior LLM-based testing approaches, the framework integrates schema-aware prompt engineering and dual-model orchestration to ground the generation process in both functional intent and technical data constraints. The methodology is distinguished by its context-aware prompt engineering, which injects a frozen database schema to ground the models, and its ability to format outputs for the TestRigor BDD 2.0 platform. This schema-grounded and orchestrated workflow enables the systematic translation of informal User Stories into executable Behavior-Driven Development (BDD) acceptance tests, reducing ambiguity and improving semantic correctness. Experimental results on a real-world dataset of fifty User Stories show the framework reduces manual test design effort by eighty per cent, achieves a four point seven five (out of five) average quality rating from human experts, and produces BDD scripts with a ninety-one point nine per cent functional correctness pass rate. These results demonstrate that orchestrated, schema-aware Generative AI can operate as a reliable co-assistant for QA teams, improving efficiency while maintaining high standards of quality and executability.

Instituciones: Universidad Peruana de Ciencias Aplicadas